Video networks is an emerging interdisciplinary field with significant and exciting scientific and technological
challenges. It has great promise in solving many real-world problems and enabling a broad range of applications,
including smart homes, video surveillance, environment and traffic monitoring, elderly care, intelligent
environments, and entertainment in public and private spaces. This paper provides an overview of the design of a
wireless video network as an experimental environment, camera selection, hand-off and control, anomaly detection.
It addresses challenging questions for individual identification using gait and face at a distance and present new
techniques and their comparison for robust identification.

This paper presents a new approach for norm bounded continuous-time uncertain switch systems such as BTT vehicle.
Firstly, the flight trajectory has been divided into several intervals according to flight attitude. Meanwhile, the nominal
model could be obtained. Secondly, based on the linear matrix inequality (LMI), combining the guaranteed cost control
theory, the state feedback guaranteed cost control law is proposed. Then, to reduce the external disadvantage influence
on vehicle, the RBF-NN is used to compensate the intensive aerodynamic disturbance. Finally, taking BTT vehicle as the
research objective, the final results of the simulation are proved with effectiveness for the proposed design approach.

Selective visual attention can direct our gaze rapidly towards objects of interest in the view. Better coverage of target
region for attention can better serve for recognition. A novel method for evaluating how well the attended regions
contribute to the recognition of the target based on SIFT descriptors is proposed in this paper. The method is used to
evaluate the attended regions extracted by some visual attention mechanisms on real remote sensor images with different
geometric and photometric transformations and for different scene types. The evaluation method proposed in this paper
give an explicit, accurate and robust expression about the attended region in visual attention mechanisms and we believe
this method could be applicable in visual attention mechanisms in the future work.

In a static monocular camera system, to gain a perfect 3D human body posture is a great challenge for Computer Vision
technology now. This paper presented human postures recognition from video sequences using the Quantum-Inspired
Immune Cloning Algorithm (QICA). The algorithm included three parts. Firstly, prior knowledge of human beings was
used, the key joint points of human could be detected automatically from the human contours and skeletons which could
be thinning from the contours; And due to the complexity of human movement, a forecasting mechanism of occlusion
joint points was addressed to get optimum 2D key joint points of human body; And then pose estimation recovered by
optimizing between the 2D projection of 3D human key joint points and 2D detection key joint points using QICA,
which recovered the movement of human body perfectly, because this algorithm could acquire not only the global
optimal solution, but the local optimal solution.

We develop a novel approach for object detection and location task. This paper proposed a novel method to represent
local regions around keypoints, called lifetime. Lifetime of a keypoint is used to describe its stability. Together with
geometric relationships extractor, lifetime representations are embedded into a bag-of-features framework. The
framework has following properties. First, the keypoints are represented as the lifetime rather than vector-quantized.
Second, a simple and computationally efficient spatial pyramid structure is used to extract the geometric relationships
between the keypoints. We demonstrate the efficacy of the proposed approach on UIUC car dataset. The experimental
results showed that our approach has an excellent performance for object detection and localization.

In this paper, a classification method of four moving objects including vehicle, human, motorcycle and bicycle in
surveillance video was presented by using machine learning idea. The method can be described as three steps: feature
selection, training of Support Vector Machine(SVM) classifier and performance evaluation. Firstly, a feature vector to
represent the discriminabilty of an object is described. From the profile of object, the ratio of width to height and
trisection ratio of width to height are firstly adopted as the distinct feature. Moreover, we use external rectangle to
approximate the object mask, which leads to a feature of rectangle degree standing for the ratio between the area of
object to the area of external rectangle. To cope with the invariance to scale, rotation and so on, Hu moment invariants,
Fourier descriptor and dispersedness were extracted as another kind of features. Secondly, a multi-class classifier were
designed based on two-class SVM. The idea behind the classifier structure is that the multi-class classification can be
converted to the combination of two-class classification. For our case, the final classification is the vote result of six twoclass
classifier. Thirdly, we determine the precise feature selection by experiments. According to the classification result,
we select different features for each two-class classifier. The true positive rate, false positive rate and discriminative
index are taken to evaluate the performance of the classifier. Experimental results show that the classifier achieves good
classification precision for the real and test data.

Due to vast growth of image databases, scene image classification methods have become increasingly important in
computer vision areas. We propose a new scene image classification framework based on combined feature and a latent
semantic model which is based on the Latent Dirichlet Allocation (LDA) in the statistical text literature. Here the model
is applied to visual words representation for images. We use Gibbs sampling for parameter estimation and use several
different numbers of topics at the same time to obtain the latent topic representation of images. We densely extract
multi-scale patches from images and get the combined feature on these patches. Our method is unsupervised. It can also
well represent semantic characteristic of images. We demonstrate the effectiveness of our approach by comparing it to
those used in previous work in this area. Experiments were conducted on three often used image databases, and our
method got better results than the others.

Currently, keyboards, mice, wands and joysticks are still the most popular interactive devices. While these devices
are mostly adequate, they are so unnatural that they are unable to give players the feeling of immersiveness.
Researchers have begun investigation into natural interfaces that are intuitively simple and unobtrusive to the
user. Recent advances in various signal-processing technologies, coupled with an explosion in the available
computing power, have given rise to a number of natural human computer interface (HCI) modalities: speech,
vision-based gesture recognition, etc. In this paper we propose a natural three dimensional (3D) game interface,
which uses the motion of the player fists in 3D space to achieve the control of sixd egree of freedom (DOFs). And
we also propose a real-time 3D fist tracking algorithm, which is based on stereo vision and Bayesian network.
Finally, a flying game is used to test our interface.

Ng-Jordan-Weiss (NJW) method is one of the most widely used spectral clustering algorithms. For a clustering problem
with K clusters, this method clusters data using the largest K eigenvectors of the normalized affinity matrix derived from
the data set. However, the top K eigenvectors are not always the most important eigenvectors for clustering. In this
paper, we propose an eigenvector selection method based on an ensemble of multiple eigenvector rankings (ESEER) for
spectral clustering. In ESEER method, first multiple rankings of eigenvectors are obtained by using the entropy metric,
which is used to measure the importance of each eigenvector, next the multiple eigenvector rankings are aggregated into
a single consensus one, then the first K eigenvectors in the consensus ranking list are adopted as the selected
eigenvectors. We have performed experiments on artificial data sets, standard data sets of UCI repository and
handwritten digits from MNIST database. The experimental results show that ESEER method is more effective than
NJW method in some cases.

Purpose: To generate a three-dimensional (3D) finite element (FE) model of human thorax which may provide the basis
of biomechanics simulation for the study of design effect and mechanism of safety belt when vehicle collision. Methods:
Using manually or semi-manually segmented method, the interested area can be segmented from the VCH (Visible
Chinese Human) dataset. The 3D surface model of thorax is visualized by using VTK (Visualization Toolkit) and further
translated into (Stereo Lithography) STL format, which approximates the geometry of solid model by representing the
boundaries with triangular facets. The data in STL format need to be normalized into NURBS surfaces and IGES format
using software such as Geomagic Studio to provide archetype for reverse engineering. The 3D FE model was established
using Ansys software. Results: The generated 3D FE model was an integrated thorax model which could reproduce
human's complicated structure morphology including clavicle, ribs, spine and sternum. It was consisted of 1 044 179
elements in total. Conclusions: Compared with the previous thorax model, this FE model enhanced the authenticity and
precision of results analysis obviously, which can provide a sound basis for analysis of human thorax biomechanical
research. Furthermore, using the method above, we can also establish 3D FE models of some other organizes and tissues
utilizing the VCH dataset.

This paper presents a new affine registration approach for planar point pattern matching. A process of parameter space
clustering is implemented to confirm a one-to-one mapping between the maximal subsets of feature point sets in images.
For a best performance, a coarse parameter space and a fine parameter space are used for vectors comparison.
Experiments show that the method can produce positive results from a small number of feature points and intensive
noise.

The development of digital aerial camera provides the possibility of acquiring highly overlapped aerial images with high
spatial resolution. In addition to its high spatial resolution that improves the capability of image interpretation, the highly
overlapped images provide favorable geometrical configuration with high redundancy. The high similarity of stereo
images is, thus, beneficial to the reliable image matching. Hence, the 3-D point clouds from the image matching have the
great potential in 3-D modeling. The topographic maps provide the distinct boundaries for building modeling. The
strategies of building reconstruction with existing topographic maps may improve the quality, cost, and efficiency for
building modeling. The objective of this investigation is to integrate highly overlapped aerial images and building
boundaries from topographic map. The proposed semi-automatic method includes 3-D lines extraction and polyhedral
model generation. In the beginning, the operator locates the initial lines by a graphical user interface. The initial lines are
refined by stereo images. The precise 3-D lines are processed into a 3-D modeling by an inference engine. The
experimental results indicate that the proposed method may reconstruct the 3-D building model effectively.

This paper is a study, based on the limitation of human vision characteristic, of image recognition through the take
account of correction factor. Those aspects that have been explored focus on human eye modelings, including human
vision recognition characteristics and various mathematical modeling verify. By using Modulation Transfer Function
(MTF) curve evaluation recognition capability on the studied models, an optimum recognition model most compatible
to human eye physiology is summed up.

The conventional display can show only one screen, but it is impossible to enlarge the size of a screen, for
example twice. Meanwhile the mirror supplies us with the same image but this mirror image is usually upside
down. Assume that the images on an original screen and a virtual screen in the mirror are completely different
and both images can be displayed independently. It would be possible to enlarge a screen area twice. This
extension method enables the observers to show the virtual image plane and to enlarge a screen area twice.
Although the displaying region is doubled, this virtual display could not produce 3D images. In this paper, we
present an extension method using a unidirectional diffusing image screen and an improvement for displaying a
3D image using orthogonal polarized image projection.

In going from two-class to multi-class classification, most boosting algorithms have been restricted to reducing multiclass
problem to multiple two-class problems. In the paper, a direct multi-class AdaBoost algorithm is adopted to face
recognition. Then the weighted classification trees are extended from stumps as weak learners to fulfill the multi-class
learning. The multi-class boosting algorithm has the following features: A K-class classification problem is treated
simultaneously without reducing it to multiple binary classification problems; only one lost function per iteration is fitted;
the algorithmic structure is compact and easy to implement. The experimental results both on UCI dataset and YaleA
face dataset show the meanings of the proposed algorithm.

In spite of years of research there is tremendous scope for improvement in face recognition systems. There is continuing
requirement for better face recognition systems with lesser computational complexity and higher accuracy. In this paper
a new algorithm is proposed which addresses these requirements by integrating the computationally efficient technique
of Fisher faces with Super-Resolution. The algorithm helps in identifying a person from sub-pixel shifted low resolution
facial images of those people whose high resolution images are present in our library. The focus of this paper is only on
face recognition and not on face detection and extraction. Extensive testing of the proposed algorithm was performed on
images from the Essex university Faces94 database. In our test many parameters such as the number of low resolution
input images and registration error etc. were varied and their effect on the accuracy (i.e. percentage of correct results)
and throughput of the face recognition system was studied. It is shown that this method has high accuracy and low
computational complexity and that it is robust to Gaussian blur, and salt and pepper noises due to camera and errors in
image registration through experimental results performed on Faces94 database.

A new hybrid registration approach that combines hierarchically structured quadrilateral displacement estimating and
local optical flow is proposed in this paper, for the purpose of super resolution (SR) reconstruction for visual surveillance
application. The proposed registration approach for complicated motions circumstance consists of two steps: a
hierarchical quadrilateral displacement estimation algorithm is designed to get coarse motion estimation as initial
prediction; then a local optical flow method is employed to obtain more accurate motion estimation for each pixel within
every matched quadrilateral. A ROI-based SR construction scheme using the proposed registration approach is presented
for iterative reconstruction of region of interest in the scene. Experimental results show that significant improvements are
achieved by applying our method than using previous methods, which suggests the effectiveness of the proposed method.

Support vector machines (SVMs) have become useful and universal learning machines. SVMs construct a decision
function by support vectors (SVs) and their corresponding weights. The training phase of SVMs definitely uses all
training samples, which leads to a large computational complexity for a large scale sample set. Moreover support vectors
could not be found until a quadratic programming (QP) problem is solved. Actually we know only SVs play a role in the
decision function. Hence, pseudo density estimation (PDE) is presented to extract a set of boundary vectors (BVs) which
may contain SVs. The PDE method is a variant of Parzen window method. Hyperspheres are considered as the window
functions. In our method, for each sample we construct a hypersphere with an unfixed radius. The ratio of the number of
samples contained in the hypersphere of a sample to the total training samples can be taken as the pseudo density of the
corresponding sample. The set of BVs is taken as the training input to SVMs. In doing so, it speeds the training
procedure of SVMs. It is convenient for PDE to determine its parameter. The experiments show that SVMs using PDE
have the similar generalization performance to SVMs.

Research on the generation of natural phenomena has many applications in special effects of movie, battlefield
simulation and virtual reality, etc. Based on video synthesis technique, a new approach is proposed for the synthesis of
natural phenomena, including flowing water and fire flame. From the fire and flow video, the seamless video of arbitrary
length is generated. Then, the interaction between wind and fire flame is achieved through the skeleton of flame. Later,
the flow is also synthesized by extending the video textures using an edge resample method. Finally, we can integrate the
synthesized natural phenomena into a virtual scene.

Extraction of high-resolution face image is crucial to detect suspect from low-resolution surveillance videos. Though
previously published super-resolution image reconstruction techniques could produce a qualified high-resolution image
from a set of simulated low-resolution images, but the reconstructed image from real low-resolution videos is always
blurring. Two main reasons contribute for this: the process of image registration is ill-posed in nature and the sub-pixel
information provided by the real video sequences is far less sufficient. In this paper, a joint image registration and face
pattern-based high-resolution image reconstruction algorithm was proposed to tackle these two problems. Experimental
results are also provided to demonstrate the effectiveness of the proposed algorithm.

To overcome the difficulty of reconstruction for small object, the paper addresses a series of improved methods. The
reconstruction mainly includes: orientation, measurement and modeling. The multi-view matching based on refining TIN
can combine the process of automatic measuring and modeling. Based on coarse-to-fine tragedy, it can increase
effectively precision and efficiency. The experiment results demonstrate that these approaches are effective and
applicable to reconstruction of small archeology with sufficient texture features.

The relevance vector machine is sparse model in the Bayesian framework, its mathematics model doesn't have
regularization coefficient and its kernel functions don't need to satisfy Mercer's condition. RVM present the good
generalization performance, and its predictions are probabilistic. In this paper, a hyperspectral imagery classification
method based on the relevance machine is brought forward. We introduce the sparse Bayesian classification model,
regard the RVM learning as the maximization of marginal likelihood, and select the fast sequential sparse Bayesian
learning algorithm. Through the experiment of PHI imagery classification, the advantages of the relevance machine used
in hyperspectral imagery classification are given out.

Facial expressions play important role in human communication. The understanding of facial expression is a basic
requirement in the development of next generation human computer interaction systems. Researches show that the
intrinsic facial features always hide in low dimensional facial subspaces. This paper presents facial parts based facial
expression recognition system with sparse representation classifier. Sparse representation classifier exploits sparse
representation to select face features and classify facial expressions. The sparse solution is obtained by solving l1 -norm
minimization problem with constraint of linear combination equation. Experimental results show that sparse
representation is efficient for facial expression recognition and sparse representation classifier obtain much higher
recognition accuracies than other compared methods.

This paper presents a new approach of Remotely Sensed data classification based on Variable Precision Rough
set(VPRS) and BP neural network, compared to traditional rough sets, VPRS is more robust to noise and can generate
more concise and representative classification rules of the remote sensing image. After the rules are deduced, they are
fed to the BP neural network, which results in short training time and a high classification accuracy.

This paper describes how to construct a hyper-graph model from a large corpus of multi-view images using local
invariant features. We commence by representing each image with a graph, which is constructed from a group of
selected SIFT features. We then propose a new pairwise clustering method based on a graph matching similarity
measure. The positive example graphs of a specific class accompanied with a set of negative example graphs are
clustered into one or more clusters, which minimize an entropy function with a restriction defined on the
F-measure( 2/(1recall+1/ precision) ). Each cluster is implified into a tree structure composed of a series of irreducible graphs,
and for each of which a node co-occurrence probability matrix is obtained. Finally, a recognition oriented class specific
hyper-graph(CSHG) is automatically generated from the given graph set. Experiments are performed on over 50K
training images spanning ~500 objects and over 20K test images of 68 objects. This demonstrates the scalability and
recognition performance of our model.

It is necessary to reconstruct a large-scale landing-site mapping by recovering and registering the local scenes into a
uniform annular scene for planetary exploration missions. This paper proposed a global relax iterative optimization
method to registering the local scenes into a uniform annular scene. For this scheme, the transform matrix between any
two adjacent 3D local scenes is fitted based on Carley transform. Subsequently, these local 3D scenes are registered into
a uniform coordinate system using relax iterative optimization method. This optimization method has been tested on the
image sequence of outdoor scenes. Experimental results show that the global registration means error decreases
significantly from 1.33 meters to 0.002 meters in 47 images.

Independent component analysis (ICA) provides an efficient approach to characterizing higher-order statistical
relationships in texture images. For the classification of textures based on ICA, a fundamental problem lies on the
selection of ICA features which are desired to maximize the separability between classes. In this paper, the efficiency of
various ICA features for texture classification is investigated, which involves ICA coefficients and their various statistics
with a focus on the higher-order statistics to take into account the non-Gaussian property of ICA coefficients. By
evaluating the ICA features on the classification of twenty-five classes of Brodatz texture images, it has been shown that
the higher-order statistics of ICA coefficients offer efficient discrimination of textures and the combination of variance,
skewness and kurtosis is a better alternative to the previously reported ICA features. By comparing the performance of
ICA features to their principal component analysis (PCA) counterparts, it is further revealed that the advantage of ICA
for texture classification can be obtained by using the higher-order statistics of ICA coefficients.

A novel object tracking algorithm for FLIR imagery based on mean shift using multiple features is proposed to improve
the tracking performance. First, the appearance model of infrared object is represented in the combination of gray space,
LBP texture space, and orientation space with different feature weight. And then, the mean shift algorithm is employed to
find the object location. An on-line feature weight update mechanism is developed based on Fisher criteria, which measure
the discrimination of object and background effectively. Experiment results demonstrate the effectiveness and robustness
of the proposed method for object tracking in FLIR imagery.

This paper focus on TV news programs and design a content-based news video browsing and retrieval system, NVRS,
which is convenient for users to fast browsing and retrieving news video by different categories such as political,
finance, amusement, etc. Combining audiovisual features and caption text information, the system automatically
segments a complete news program into separate news stories. NVRS supports keyword-based news story retrieval,
category-based news story browsing and generates key-frame-based video abstract for each story. Experiments show
that the method of story segmentation is effective and the retrieval is also efficient.

Inspired by the idiotypic network theory, a new artificial immune network classifier for SAR image is proposed in this
paper. In the proposed algorithm, only one B-cell instead of many B-cells is used to denote a class so as to reduce the
scale of network as well as avoid the suppression operation between B-cells; moreover, a new affinity function based on
the correct rate is proposed to realize antigen priority based the evaluation strategy. The proposed algorithm has been
extensively compared with Fuzzy C-means (FCM), Multiple-Valued Immune Network algorithm (MVIN), and Clonal
Selection Algorithm for classifier (CSA) over two SAR images. The result of experiment indicates the superiority of the
algorithm over FCM, MVIN and CSA on classification accuracy and robustness.

We propose a new distance estimation technique by boosting and apply it to enhance the effectiveness of classifier when
the training set is insufficient. The proposed method is called Boosted Distance based on local and global dissimilarity
representation (BDLGDR). It is a modified method of Boosted Distance. Rather than simply differentiating the feature
vectors, we calculate a new dissimilarity representation of each couple of feature vectors. This new dissimilarity
representation contains two parts: local dissimilarity representation part and global dissimilarity representation part. The
proposed method does not only achieve high classification accuracy when the training set is insufficient but when the
number of training set is sufficient it also can achieve as high accuracy as AdaBoost. The method has been thoroughly
tested on several databases of high-resolution (1.25m) Terra-SAR images. In the first experiment, we decreased the
number of the training sample per class from 10 to 1. The result showed that the proposed method outperformed both
Boosted Distance and AdaBoost. In the second experiment, we used sufficient training samples. The experimental result
illuminated that the proposed method performed at least as well as AdaBoost and needed fewer iteration rounds to
converge than Boosted Distance.

This paper proposes an ant colony optimization (ACO) based approach for point pattern matching (PPM) under affine
transformation. In the paper, the point sets matching problem is formulated as a mixed variable (binary and continuous)
optimization problem. The ACO is used to search for the optimal transformation parameters. There are two contributions
made in this paper. Firstly, we manage to modify the original ACO method by combining it with the leastsquares
method. Thus, it can handle with the continuous spatial mapping parameters searching. Secondly, we introduce a
threshold to correspondence finding, which rejects outliers and enhances veracity while using "Nearest Neighbors
Search". Experiments demonstrate the validity and robustness of the algorithm.

A novel fuzzy approach for the detection of traffic signs in natural environments is presented. More than 3000 road
images were collected under different weather conditions by a digital camera, and used for testing this approach. Every
RGB image was converted into HSV colour space, and segmented by the hue and saturation thresholds. A symmetrical
detector was used to extract the local features of the regions of interest (ROI), and the shape of ROI was determined by a
fuzzy shape recognizer which invoked a set of fuzzy rules. The experimental results show that the proposed algorithm is
translation, rotation and scaling invariant, and gives reliable shape recognition in complex traffic scenes where clustering
and partial occlusion normally occur.

Autonomous service on orbit is a new developing trend of space service, in order to realize on orbit servicing, the
problem of autonomous relative navigation needs to be solved. On the foundation of several typical schemes used by
American, Russian as well as ESA etc., the main conception of our autonomous video navigation system is determined.
The beacon system is composed of five beacon lamps, and has four invariant features relative to transformations of
rotation, translation and scale. Two sets of double camera systems constituted of three fixed lens cameras with short
focus are applied to the longer distance and the shorter distance respectively. Three kinds of measures are put forward to
suppress the interference of miscellaneous lights. Then, the study and simulation of beacon recognition as well as the
determination of relative positions and attitudes are expounded in this paper, the algorithm flow chart and corresponding
simulation results are given. A kind of spiral capturing algorithm is used to increase the capturing efficiency, and the
beacon recognition algorithm is designed according to the invariant features of beacon system to increase the recognition
capability. In the determination of relative positions and attitudes, monocular algorithm and binocular algorithm are
combined to ensure the reliability. Simulation results have verified the feasibility of the design of autonomous video
navigation sensor and the autonomous video navigation techniques.

Celestial spectrum recognition is an indispensable part of any workable automated data processing system of
celestial objects. Many methods have been proposed for spectra recognition, in which most of them concerned about
feature extraction. In this paper, we present a Bayesian classifier based on Kernel Density Estimation (KDE) which
is composed of the following two steps: In the first step, linear Principle Component Analysis (PCA) is used to
extract features to decrease computational complexity and make the distribution of spectral data more compact and
useful for classification. In the second step, namely classification step, KDE and Expectation Maximum (EM)
algorithm are used to estimate class conditional density and the bandwidth of kernel function respectively. The
experimental results show that the proposed method can achieve satisfactory performance over the real observational
data of Sloan Digital Sky Survey (SDSS).

This paper developed two learning procedure, respectively, based on the orthogonal least squares (OLS) method and
the "Innovation-Contribution" criterion (ICc) proposed newly. The orthogonal use of the stepwise-regression algorithm
of the ICc mages the model structure independent of the selected term sequence and reduces the cluster region further as
compared with orthogonal least squares (OLS). as the Bayesian information criteria (BIC) method is incorporate into the
clustering process of the ICc, except for the widths of Gaussian functions, it has no other parameter that need tuning ,but
the user is required to specify the tolerance ρ, which is relevant to noises and will be difficult to implement in the real
system, for the OLS algorithm. The two algorithms are employed to the Radial Basis Function Neural Networks
(RBFNN) to compare its performance for different noise nonlinear dynamic systems. Experimental results show that
they provide an efficient approximation to the required results for fitting models, but the clustering procedures of the ICc
is substantially better solutions than does the OLS.

A fast and flexible technique of 3D modeling of building based on image sequence has been proposed in this paper. It
firstly describes the importance of the study in this area, then gives a detailed analysis of each step of the whole
reconstruction process, including homonymy points selection, determination of key points' relationship, the solution
method of splicing among points appeared on different stereo images, texture mapping and so on. Finally, real data has
been used to validate the proposed technique, using VC++6.0 and OpenGL to realize the visualization of the buildings
artificial interactively, and satisfied results have been obtained demonstrating the effectiveness and flexibility of the
approach.

In this paper, a center matching scheme is proposed for constructing a consensus function in the k-means cluster
ensemble learning. Each k-means clusterer outputs a sequence with k cluster centers. We randomly select a cluster center
sequence as a reference one, and then we rearrange the other cluster center sequences according to the reference
sequence. Then we label the data using these matched cluster center sequences. Hence we get multiple partitions or
clusterings. Finally, multiple clusterings are combined to the best labeling by using combination rules, such as the
majority voting rule, the weighted voting rule and the selective weighted voting rule. Experimental results on 7 UCI data
sets show that our ensemble methods could improve the clustering results effectively.

In this paper, we first analyzed the possible change of support vector set after new samples are added, then presented a
new support vector machine incremental learning algorithm. This algorithm reconstructed SVM classifier through the
selection of training samples in incremental learning based on change regularity of support vectors after new samples are
added. Finally, the algorithm has a higher classification accuracy than traditional SVM incremental algorithms through
experimental verification.

The human vision system has visual functions for viewing 3D images with a correct depth. These functions are
called accommodation, vergence and binocular stereopsis. Most 3D display system utilizes binocular stereopsis.
The authors have developed a monocular 3D vision system with accommodation mechanism, which is useful
function for perceiving depth.

Since SVM is very sensitive to outliers and noises in the training set and the fuzzy feature exists in remote sensing
images, we hereby studied fuzzy support vector machine based on the affinity among samples. The fuzzy membership is
defined by not only the relation between a sample and its cluster center, but also the affinity among samples. A method
defining the affinity among samples is proposed using a sphere with minimum volume while containing maximum of the
samples. Then, the fuzzy membership is defined according to the position of samples in sphere space, which
distinguished between the valid samples and the outliers or noises. The experiment results show, it discriminates support
vectors with noise or outliers much better. Experimental results show that our method performs better than SVM in
classification of the images in Wuhan and with less influnence by the noise interference.

We present a new approach to face detection with skin color mixture models and asymmetric AdaBoost. First, non-skin
color pixels of the input image are rapidly removed based on skin color mixture models in RGB and YCbCr
chrominance spaces, from which we extract candidate face regions. Then, face detection with fast asymmetric AdaBoost
is carried out in candidate face regions where ratios of pixels of skin color to non-skin color are beyond certain
thresholds. To further reduce the computational cost, the integral image technique is employed to calculate ratios of
pixels of skin color to non-skin color in candidate face regions. Finally, false alarms are gradually merged and removed
by relative geometric relation and the rate of skin color pixels on the intersection line of candidate face regions.
Experimental results show that our proposed method reduces significantly false alarms and the processing time while
achieves detection rates of more than 99%.

Eye corner detection is important for eye extraction, face normalization, other facial landmark extraction and so on. We
present a feature-based method for eye corner detection from static images in this paper. This method is capable of
locating eye corners automatically. The process of eye corner detection is divided into two stages: classifier training and
classifier application. For training, two classifiers trained by AdaBoost with Haar-like features, are skillfully designed to
detect inner eye corners and outer eye corners. Then, two classifiers are applied to input images to search targets. Eye
corners are finally located according to two eye models from targets. Experimental results tested on BioID face database
and our own database demonstrate that our method obtains a high accuracy under clutter conditions.

For the special shape of the tunnel and the limitation to the image sequences capturing inside the tunnel, conventional
stereo algorithms are difficult to apply for 3D reconstruction of the tunnel. This paper presents an automatic
reconstruction method for 3D tunnel reconstruction from the monocular image. The proposed method assumes one 3D
wireframe model of the tunnel can be easily obtained by a few known data. Any lines on this model can be expressed
with Plucker matrix, by using detected lines on the image, the line projection matrix can be obtained. Then the whole 3D
wireframe model can be mapped onto the image, so that a texture can be extracted for the 3D wireframe model, then one
textured 3D tunnel model is obtained. The experimental results show that our method can be easily and effective
performed in the practice.

In this paper, we present a new member of the biometrics family, i.e. nose pores, based on DLPP. Little work has been
done on nose pores as a biometric identifier. In this work, we made use of a database of nose pore images obtained over
a long period to examine the performance of nose pores as a biometric identifier. First, the midpoint and midline were
located and taken as reference for the ROI segmentation after nose image was segmented. Second, nose pore feature was
filtered by LOG filters. Third, the extracted pore was projected to low dimensional space by DLPP. Finally, the feature
in low dimension was classified by Euclidean distance. This research showed that the nose pore is a promising candidate
for biometric identification and deserves further research. The experimental results based on the unique nose pores
database demonstrated that nose pores can give a 91.91% correct recognition rate for biometric identification, which
showed this biometric identifier's feasibility and effectiveness. Compared with result without using DLPP, the feature
extraction by DLPP was more precise.

The recent increase of space threats yields the idea of using the existing star trackers to perform surveillance of space
objects from space. In the missions, due to the observer attitude dynamics, smearing affects the observed stars on the
image in space surveillance. Besides, the reflecting flying space objects or debris as spurious stars affects the attitude
determination. These are devastating for most star identification algorithms in star trackers. To resolve the problems, this
paper defines a star pattern, called Flower code, which is composed of angular distances and circular angles as the
characteristics of the pivot star. The angular distances are used for initial lookup table match. Moreover, the circular
angles are used for the cyclic dynamic match between the sensor pattern and the pattern candidates from the initial
match. The focus of the results is the evaluation of the influence of the reflecting flying spacecraft or debris as spurious
stars and the attitude dynamics of the observer spacecraft, on the performance of the algorithms. A number of
experiments are carried out on simulated images. The results demonstrated that the proposed method is efficient and
robust.

A new nonlinear control strategy incorporated the dynamic inversion method with wavelet neural networks is presented
for the nonlinear coupling system of Bank-to-Turn(BTT) missile in reentry phase. The basic control law is designed by
using the dynamic inversion feedback linearization method, and the online learning wavelet neural network is used to
compensate the inversion error due to aerodynamic parameter errors, modeling imprecise and external disturbance in
view of the time-frequency localization properties of wavelet transform. Weights adjusting laws are derived according to
Lyapunov stability theory, which can guarantee the boundedness of all signals in the whole system. Furthermore, robust
stability of the closed-loop system under this tracking law is proved. Finally, the six degree-of-freedom(6DOF)
simulation results have shown that the attitude angles can track the anticipant command precisely under the
circumstances of existing external disturbance and in the presence of parameter uncertainty. It means that the
dependence on model by dynamic inversion method is reduced and the robustness of control system is enhanced by
using wavelet neural network(WNN) to reconstruct inversion error on-line.

An airborne vehicle must avoid obstacles like towers, fences, tree branches, mountains and building across the flight path.
So the ability to detect and locate obstacles using on-board sensors is an essential step in the autonomous navigation of
aircraft low-altitude flight. In this paper, a novel passive range method using conditional random field (CRF) is presented
to map the 3D scene in front of a moving aircraft with image sequences obtained from a forward-looking imaging sensor.
Finally, An dynamic graph cuts method was presented for the CRF model to recursively update thedepth map.
Experimental data demonstrates the effectiveness of our approach.

MLESAC is one of the most widely used robust estimators in the field of computer vision. A shortcoming of this method
is its low efficiency. An enhancement of MLESAC, the locally optimized MLESAC (LO-MLESAC) is proposed.
LO-MLESAC adopts the same sample strategy and likelihood theory as the previous approach and an additional
generalized model optimization step is applied to the models with the best quality. Results are given for several image
sequences. It is demonstrated that this method gives results superior to original MLESAC.

This paper studies statistics based scene change detection in the video streaming scenario, and three
scene feature metrics including histogram distance, chi-square distance, and Bhattacharyya distance have been
investigated. With the unique characteristics of triangular inequality and non-singularity, Bhattacharyya distance
has been proposed as a viable scene change metrics. It outperforms much better than the other two in that it
calculates and maximizes the feature vector distance between multi-modal clusters in a hyper-sphere space. The
experiments are conducted and the precision recall statistics are compared, and the results support our analysis.

With the advent of information age, especially with the rapid development of network, "information explosion"
problem has emerged. How to improve the classifier's training precision steadily with accumulation of the samples is the
original idea of the incremental learning. Support Vector Machine (SVM) has been successfully applied in many pattern
recognition fields. While its complex computation is the bottle-neck to deal with large-scale data. It's important to do
researches on the SVM's incremental learning. This article proposes a SVM's incremental learning algorithm based on
the filtering fixed partition of the data set. This article firstly presents "Two-class problem"s algorithm and then
generalizes it to the "Multiclass problem" algorithm by the One-vs-One method. The experimental results on three types
of data sets' classification show that the proposed incremental learning technique can greatly improve the efficiency of
SVM learning. SVM Incremental learning can not only ensure the correct identification rate but also speedup the training
process.

The traditional classification method based on the spectral information in pixel level faces the problem of spectrum
confusion. Pixels on multi-temporal image show dependencies in both the spatial and temporal domains besides spectral
information. When spectral information has limited discriminative power, spatial-temporal dependencies can help to
remove the spectral confusion. Two ETM+ images in different seasons after processing are used and supervised
classification algorithm-the maximum likelihood classification (MLC) is used to initialize the algorithm proposed in this
article. Then a Markov Random Fields (MRF) model is used to model the spatial-temporal contextual prior probabilities
of images. Lastly the likelihood estimates of spectral observation from MLC and conditional spatial-temporal priors from
MRF are integrated into posterior estimates by Bayes rule, the optimal classification was achieved when the
classification corresponds to maximum a posteriori (MAP). The results show that MRF is an efficient probabilistic model
for analysis of spatial and temporal contextual information. A spatial-temporal classification algorithm that explicitly
integrates spectral, spatial and temporal information in multi-temporal images can achieve significant improvements over
non-contextual classification. Some errors have been avoided because of the integration of space and time information.

Since manual surface reconstruction is very costly and time consuming, the development of automatic algorithm is of
great importance. In this paper a fully automated technique based on hierarchical structure analysis of the building to
extract urban building models from LIDAR data is presented. In the processing of reconstruction, the existing automatic
algorithm can solve some simple building reconstructions, such as flat roof, gabled roof. As to complex buildings, many
researchers use external information or manual interaction for help because of the complexity of the reconstruction and
the uncertainty of the building models especially in urban areas. The contour has the characteristics of closed loop, not
intersect and deterministic topological relationship, which can be used to extract building ROI (region of interesting). A
contours tree is constructed, the topological relationships between the different contours which extracted by TIN from
the LIDAR data are established, then the relationships among each hierarchical model can be determined by the analysis
of the topological relationship among contour clusters and a component tree corresponding to the building can be
constructed by tracing the contours tree. The accurate edges of hierarchical model can be gained by the "polarized
cornerity index"-based polygonal approximation of the contour. Especially, a 3D model recognition based on 2D shape
recognition is employed. According to the characteristics of the contours, the category of the primitive parts can be
classified. We assemble the hierarchical models by using the topological relationships among layers, then, the complete
model of the building can be obtained. Experimental results show that the proposed algorithm is suitable for
automatically producing building models including most complex buildings from LIDAR data in urban areas.